Multilevel Modeling With Stat-JR SAAs: A Software Review

Minjung Kim and Hsien-Yuan Hsu
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Minjung Kim: The Ohio State University
Hsien-Yuan Hsu: University of Texas Health Science Center

Journal of Educational and Behavioral Statistics, 2019, vol. 44, issue 1, 103-121

Abstract: Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5), with regard to their use for MLM. In this article, we review the features of Stat-JR’s SAAs and illustrate how to implement SAAs, using one of the Stat-JR interfaces to analyze multilevel models for the 1982 High School and Beyond data set. Results from Stat-JR SAA are compared with the results using HLM7.01 software. We also discuss recommendations and implications for future users of SAAs.

Keywords: achievement; assessment; hierarchical linear modeling; student knowledge (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:44:y:2019:i:1:p:103-121

DOI: 10.3102/1076998618811383

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